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Low-cost High-throughput Screening of All Inorganic Materials

Low-cost High-throughput Screening of All Inorganic Materials

Royal Society of Chemistry MC13 Meeting - July 2017 - Liverpool

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Dan Davies

July 11, 2017
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Transcript

  1. Low-cost High-throughput Screening of All Inorganic Materials Daniel Davies MC13

    11th July 2017 Centre for Sustainable Chemical Technologies
  2. Overview The landscape for new inorganic materials is too vast

    to explore using high- throughput experimental or first principles techniques. However, it can be navigated using low-cost tools as part of a hierarchy of screening steps. [LOW COST] [HIGH COST]
  3. Computational Materials: Present INPUT OUTPUT Structure Properties

  4. Computational Materials: Future INPUT OUTPUT Properties Composition Structure

  5. • High-throughput computation • Data mining • Machine learning •

    Chemical knowledge “Materials Genome” Exploiting data on existing materials www.materialsproject.org www.oqmd.org www.nomad-coe.eu
  6. Constructing a sensible search space D. W. Davies et al.,

    Chem, 1, 617 (2016) SMACT http://www.github.com/WMD-Group/SMACT 1. Element combinations: 2. Charge neutrality: 3. Electronegativity balance: Ax By Cz (x,y,z ≤ 8) xqA +yqB +zqC = 0 cation < anion 103
  7. There is plenty of room to explore the SMACT search

    space D. W. Davies et al., Chem, 1, 617 (2016) http://www.github.com/WMD-Group/SMACT
  8. Element Combinations Chemical Filters Structure Prediction Structure Prediction Property Calculation

    Final Candidates [LOW COST] [HIGH COST] A low-cost workflow
  9. Element Combinations Chemical Filters Example: New photoactive chalcohalides Ax By

    Cz (x,y,z ≤ 8) chalcohalides Valence Conduction Eg
  10. Valence Conduction Eg Element Combinations Chemical Filters Example: New photoactive

    chalcohalides Ax By Cz (x,y,z ≤ 8) chalcohalides Solid state energy (SSE) scale1 Herfidahl- Hirschman Index (HHIR )2 1. B. Pelatt et al., J. Am. Chem. Soc., 133, 16853 (2011) 2. M. Gaultois et al., Chem. Mat., 25, 2911 (2013)
  11. D. W. Davies et al., Chem, 1, 617 (2016) From

    ~32 million possible ternary compositions to the top few candidates Sustainability index More sustainable Less sustainable
  12. D. W. Davies et al., Chem, 1, 617 (2016) From

    ~32 million possible ternary compositions to the top few candidates Sustainability index More sustainable Less sustainable
  13. Compositional Combinations Chemical Filters Structure Prediction Structure Prediction Property Calculation

    Final Candidates We need to arrange the atoms in space 1. 2. [LOW COST] [HIGH COST]
  14. From composition to crystal structure 1. G. Hautier et al.,

    Inorg. Chem, 50, 656 (2011) 2. D. W. Davies et al., In Preparation (2017) 1. By analogy with existing structures1
  15. From composition to crystal structure 1. G. Hautier et al.,

    Inorg. Chem, 50, 656 (2011) 2. D. W. Davies et al., In Preparation (2017) 1. By analogy with existing structures1
  16. From composition to crystal structure 1. G. Hautier et al.,

    Inorg. Chem, 50, 656 (2011) 2. D. W. Davies et al., In Preparation (2017) < 100 meV/atom above the hull 18.2 meV
  17. From composition to crystal structure D. W. Davies et al.,

    In Preparation (2017) 2. Global search Phonon calculations carried out to check dynamic stability 18.2 meV/atom 18.0 meV/atom Data mining Global optimisation
  18. And finally.. optoelectronic properties [Hybrid DFT] Accurate electronic structure calculations

    2.15 eV
  19. http://www.github.com/WMD-Group/SMACT Acknowledgements • Aron and the Walsh Materials Design Group

    • Keith Butler, Adam Jackson and Jonathan Skelton, Congwei Xie Future directions • Add descriptors to search for a wider variety of properties • Incorporate machine learning models into the low-cost steps @danwdavies
  20. Appendix 1